Cancer evolution assessment using artificial neural networks.
Cipriana Ştefănescu, Lucian Vasile Boiculese, V Rusu, Mihaela Costin, D Hountis, Elena Musat · PubMed · 2005
Artificial Neural Networks have demonstrated good utility to establish disease prognostic, if there are correctly trained. Our aim was to realize Artificial Neural Networks in different cancer types, to evaluate time survival. For the beginning this was applied for breast cancer. The conclusion is that the homogeneity of data entrance sets, the number, and their coding in relation with their importance for cancer prognostic are decisive for the results of the trained ANN. These results can be useful in the physician clinical decision.